2 papers
cs.DC2025
FLAME: A Serving System Optimized for Large-Scale Generative Recommendation with Efficiency
Xianwen Guo, Bin Huang, Xiaomeng Wu +6
Generative recommendation (GR) models possess greater scaling power compared to traditional deep learning recommendation models (DLRMs), yet they also impose a tremendous increase…
cs.IR2025
Climber: Toward Efficient Scaling Laws for Large Recommendation Models
Songpei Xu, Shijia Wang, Da Guo +5
Transformer-based generative models have achieved remarkable success across domains with various scaling law manifestations. However, our extensive experiments reveal persistent ch…